Method and system of measuring characteristics of an organ

ABSTRACT

The invention provides a method of measuring characteristics of an organ or part thereof from multiple images of the organ or part thereof, the method comprising the steps of defining the spatial position of at least two of the images; defining a reference model of the organ or part thereof scaled according to the distance between reference markers on the images; defining one or more boundary guide points associated with one or more images for which the spatial positions have been defined; converting the guide points to three-dimensional coordinates; defining an estimate model by fitting the reference model to the guide points; and calculating the characteristics from the estimate model. The invention also provides a system and a computer program for measuring characteristics of an organ or part thereof of a subject from multiple images of the subject&#39;s organ or part thereof.

FIELD OF THE INVENTION

The invention relates to a method and system of measuringcharacteristics of an organ or part thereof. The method and system ofthe invention are particularly suited to measuring cardiac function,volume, and/or mass of a ventricle of the heart of a subject frommultiple image slices obtained by magnetic resonance imaging (MRI). Themethod and system of the invention may also be used to calculate thevolume and/or mass and other characteristics of other organs such as alung, kidney or brain, to measure the position of the wall of a bloodvessel for the purposes of analysis of flow, or to calculate the volumeand/or mass of a bone, tumour or cyst.

BACKGROUND TO INVENTION

Ventricular mass, volumes and wall thickness at end diastole and endsystole are essential clinical parameters for diagnosis and managementof many cardiac diseases. Magnetic resonance imaging (MRI) may be usedto estimate heart wall motion by reconstructing the shape and motion ofthe left ventricle.

MRI is also able to provide accurate and precise estimations ofventricular mass, volume and wall thickness, since it is a true3-dimensional method which is not dependent on geometric assumptions andis not limited in the position or orientation of the possible images,unlike other methods, such as for example echocardiography or computedtomography.

Recent advances in MRI allow the acquisition of 10 to 20 MRI images orslices in short and long axis or arbitrary orientations, each with 10 to25 frames through the cardiac cycle in real time, or ten to fifteenminutes or less, which is a clinically acceptable time. Previous studieshave shown that the summation of areas outlined in short axis MRI slicesgives more accurate and reproducible estimates of volume thanechocardiography or LV angiography.

A major limitation of the MRI slice summation method is the prohibitivetime required to outline the endocardial and epicardial boundaries ofthe left ventricle in each slice. This severely limits application ofthe use of the technique to routine clinical care.

In the past, many semi-automated image segmentation algorithms have beenapplied to this problem, but these solutions are frequently notsufficiently robust and accurate for routine clinical use. In particularthe image pixel intensities are insufficient to adequately constrain thesegmentation problem, due to the limited temporal and spatialresolution, presence of image artifacts, and lack of contrast betweenblood and muscle. The amount of time spent on manual editing andcorrection of contours obtained from these previous solutions rendersautomated methods nearly as slow as manual contouring in clinicalpractice.

Other techniques apply model fitting techniques to estimatecharacteristics of organs such as the left ventricle. T McInerney and DTerzopoulos in “A Dynamic Finite Element Surface Model for Segmentationand Tracking in Multidimensional Medical Images with Application toCardiac 4D Image Analysis”, Computerized Medical Imaging and Graphics19:69–83; 1995 describe a deformable “balloon” model that istopologically isomorphic to a sphere for use in estimating volume andmotion of the left ventricle. PCT international patent publication WO99/18450 to Philips AB titled “Method of and Device for Imaging anObject by means of Magnetic Resonance” describes the use of an ellipsoidto model the left ventricle. Both techniques require the use of edgedetection algorithms and in doing so suffer from the disadvantagesdiscussed above.

SUMMARY OF INVENTION

In broad terms in one aspect the invention comprises a method ofmeasuring characteristics of an organ or part thereof of a subject frommultiple images of the subject's organ or part thereof, the methodcomprising the steps of defining the spatial position of at least two ofthe slices; defining a reference model of the organ or part thereofscaled according to the distance between the slices; defining one ormore boundary guide points associated with one or more slices for whichthe spatial positions have been defined; converting the guide points tothree-dimensional coordinates; defining an estimate model by fitting thereference model to the guide points; and calculating the characteristicsfrom the estimate model.

In broad terms in another aspect, the invention comprises a system formeasuring characteristics of an organ or part thereof of a subject frommultiple image slices of the subject's organ or part thereof, the systemcomprising a memory in which is stored the spatial position of at leasttwo of the slices; reference model definition means arranged to define areference model of the organ or part thereof scaled according to thedistance between the slices; boundary guide point definition meansarranged to define one or more boundary guide points associated with oneor more slices for which the spatial positions are stored in the memory;conversion means arranged to convert the guide points tothree-dimensional coordinates; estimate model definition means arrangedto define an estimate model by fitting the reference model to the guidepoints; and calculation means arranged to calculate the characteristicsfrom the estimate model.

In yet another form the invention comprises a computer program formeasuring characteristics of an organ or part thereof of a subject frommultiple image slices of the subject's organ or part thereof, theprogram comprising storage means arranged to store the spatial positionof at least two of the slices; reference model definition means arrangedto define a reference model of the organ or part thereof scaledaccording to the distance between the slices; boundary guide pointdefinition means arranged to define one or more boundary guide pointsassociated with one or more slices for which the spatial positions arestored in the memory; conversion means arranged to convert the guidepoints to three-dimensional coordinates; estimate model definition meansarranged to define an estimate model by fitting the reference model tothe guide points; and calculation means arranged to calculate thecharacteristics from the estimate model.

BRIEF DESCRIPTION OF THE FIGURES

Preferred forms of the invention will now be described, by way ofexample, with reference to the accompanying figures in which:

FIG. 1 is a flow chart outlining a preferred form of the method of theinvention;

FIG. 2 shows the main window of a preferred form of an applicationprogram in which the invention is implemented;

FIG. 3 shows a guide points window and the preferred method of selectingthe left ventricular basal slice from this window;

FIG. 4 shows the preferred method of selecting the left ventricularapical slice from the window of FIG. 3;

FIG. 5 shows the preferred method of entering characteristics of theright ventricle from the window of FIG. 3;

FIG. 6 shows the preferred method of defining the base or mitral valveplane from the window of FIG. 3;

FIGS. 7 to 10 illustrate a method of selecting boundary guide pointsfrom the window of FIG. 3;

FIGS. 11 and 12 show a preferred window for viewing the model; and

FIGS. 13 to 15 show preferred displays of data obtained from the methodand system of the invention.

DETAILED DESCRIPTION OF PREFERRED FORMS

FIG. 1 sets out a preferred form of method of the invention.

A number of images are first obtained of the left ventricle of a subjectas indicated at. The images could be acquired from an MRI scanner, ormay alternatively be acquired by an ultra-fast CT, 3-dimensionalultrasound machine or echocardiography, or other suitable imagingmodality. The images could also be obtained from confocal microscopy,electron microscopy or histology. The images are typically 2-dimensionalcines or movies of the heart and are taken at standard orientations, forexample, short axis and long axis, or at entirely arbitrary positionsdepending on the nature of the pathology and imaging modality.

The preferred images are acquired in a number of spatial locations,having a lowest or apical slice, a highest or basal slice, one or moremiddle slices and one or more long axis slices. The preferred images areacquired in between 2 and preferably 20 spatial locations, and typically12 spatial locations. Preferably images in each of these spatiallocations are obtained at multiple frames through the cardiac cycle. Thepreferred number of frames is 10 to 25.

Conventional MRI imaging apparatus produces images having image headers.The image header in each image generally comprises an extensive datalist including patient name and scan parameters at the beginning of eachimage. The image header also provides data representing the spatialposition and temporal position of each slice or frame.

The images are loaded into memory as indicated at 112. The preferredmemory forms part of a computer having a CPU, input devices and adisplay device such as a VDU. The preferred input devices comprise akeyboard, mouse and disk drive, typically a networked magneto-opticaldisc drive and/or CD ROM drive. Images may also be transferred over anetwork. The preferred memory comprises a hard disk drive suitable forstoring the images.

The preferred computer comprises a SUN SparcStation, SGI work station,PC, or similar having at least 128 MB Ram or similar. The computer hasloaded on it suitable operating system software, such as SOLARIS, IRIX,WINDOWS or LINUX. The preferred computer is arranged to executeapplication software in which the present invention has been developed.The preferred application software is written in C++, using OpenGL,OpenInventor and Xwindows for graphical interfaces.

Referring to FIG. 2, the user is presented with a main window from whicha number of options may be selected. The option to load planes isindicated at 14 which loads the three-dimensional position of the imagesand the option to load frames is indicated at 15 which loads theposition of the images in time. This spatial and temporal information isgenerally included in the image headers. The option to load images isindicated at 16. Each separate image location is displayed in a secondwindow at the first phase of imaging simultaneously.

Various parameters may be specified by the user at the time of loadingthe images, for example Model Type, Fit Type and Data Set. The user mayalso specify directory names for directories such as a Data Directory, aModel Directory and a Script Directory.

As shown at 114 in FIG. 1, the next step is to define athree-dimensional coordinate system from two or more of the slices forwhich the spatial positions have been defined. The main window of FIG. 2presents to the user an option to enter the guide points editor. Theguide points editor loads the editor window shown in FIG. 3. The editorwindow includes panel 20 which in the preferred form displays thumbnailimages of the images stored in the memory. Panel 22 displays an enlargedimage of one of the images displayed in panel 20. The preferred formwindow shown in FIG. 3 also provides the user with the ability to zoomand pan images and to adjust brightness and contrast. The user mayselect the image to be displayed in panel 22 by, for example, clickingon one of the thumbnails displayed in panel 20.

The user first selects the basal or highest short axis slice in panel20, clicking on the thumbnail image to display the enlarged image inpanel 22. In FIG. 3 the user has selected the basal slice which isdescribed below panel 22 as the seventh short axis image obtained duringthe first time interval or frame.

The user selects the Base option indicated at 24. A prompt is displayedfor the user to pick a point to represent the base in the model'scardiac coordinate system. The user selects a point in the centre of theventricular image by clicking in panel 22. Once the desired base hasbeen selected the user selects the option to accept the base indicatedat 26.

Referring to FIG. 4, the user repeats the procedure to select the apexof the object. The user selects the Apex option indicated at 28. Aprompt is displayed for the user to pick a point representing the apexand the user then selects the point in panel 22. Once the desired apexhas been selected the user accepts the apex as indicated at 30.

If the spatial position of the basal slice and the apical slice is knownthen land marks may be determined, for example the long axis. Thesoftware calculates the length and position of the long axis by defininga line in 3-dimensional space between the two points selected by theuser.

As shown in FIG. 5, the user is not limited to defining the co-ordinatesystem on the left ventricle. By selecting the option indicated at 35the user may select different points or landmarks to analyse, forexample, the right ventricle.

As shown in FIG. 6, the user may display in panel 22 an image taken ofthe long axis of the object, as indicated by the description below panel22. The user selects the Base Points option 32 which then prompts theuser to choose two or more points on the image to define the maximumextent for estimate model and calculate volumes of, for example, themitral valve plane. The user selects the points by clicking in panel 22and once the desired points have been selected the user accepts the basepoints indicated at 34. Further options may also be provided, forexample advancing the image displayed in panel 22 to the next frame.

It will be appreciated that the user does not need to define the axis byselecting the centre of the ventricle in the basal and apical slices.The user may instead define the centre of the left ventricle in twoslices which are not the basal or apical slices. The software may thencalculate the position of the long axis by defining a line in3-dimensional space between these two points. The length of the longaxis of the left ventricle may then be calculated separately from thedistance between the basal and apical slices if the spatial position ofthe slices is known.

As shown at step 116 in FIG. 1, the software defines a reference modelof a left ventricle. The preferred reference shape closely approximatesthe generic shape of a left ventricle. The reference model may beconstructed from real patient data, but does not have to be absolutelyaccurate in terms of an individual patient. The reference model may bedefined as an analytical function, as a coordinate system, or as datapoints.

The preferred reference model is a finite element model consisting of atleast 16 elements, typically 16 to 40, each with cubic interpolation inthe circumferential and longitudinal directions. Linear interpolation isused to couple the endocardial and epicardial surfaces into a coherent3-D model. The preferred model is defined in a polar coordinate systemin which the radial coordinate of the model is fitted as a function ofthe two angular coordinates in the circumferential and longitudinaldirections respectively. The preferred initial shape of the referencemodel is a regular ellipsoid, typically a prolate spheroid, obtained bysetting the endocardial and epicardial surfaces to a constant radialvalue. The preferred reference model is scaled according to the lengthof the long axis of the left ventricle of the subject with the extent ofthe model in the longitudinal direction set to correspond to the pointidentified on the most basal slice of the left ventricle in the longaxis.

The preferred estimate model is obtained by a least squares finiteelement modelling process in which the left ventricle is divided into anumber of rectangular segments or elements. Each element defines abicubic spline surface for part of the endocardial and epicardialsurfaces of the left ventricle. It is important to ensure continuity inthe surface defined by adjacent elements. To ensure continuity, adjacentelements are constrained to have the same position and slope on eachside of the join. Ensuring continuity in this way eliminates or at leastreduces ridges and sharp transitions between adjacent elements.

Within each element, the geometric coordinate field x is given as afunction of element or material coordinates ξ by a weighted average ofnodal values:

$\begin{matrix}{{x\left( {\xi_{1},\xi_{2},\xi_{3}} \right)} = {\sum\limits_{n}{{\Psi_{n}\left( {\xi_{1},\xi_{2},\xi_{3}} \right)}\lambda^{n}}}} & (1)\end{matrix}$where λ^(n) are the nodal values, Ψ_(n) are the element basis functionswhich give the relative weighting of each nodal value, and (ξ₁, ξ₂, ξ₃)are the element co-ordinates.

The geometric field λ is defined to be the radial co-ordinates in thepolate spheroidal coordinate system:x=f cos h(λ)cos(μ)y=f sin h(λ)sin(μ)cos(θ)z=f sin h(λ)sin(μ)sin(θ)  (2)where (λ, μ, θ) are the radial, longitudinal and circumferentialco-ordinates of the polar system and (x, y, z) are the correspondingrectangular Cartesian co-ordinates. The focal length f of the polatesystem is preferably chosen so that the λ=1 surface gives a good initialapproximation of the left ventricular epicardial surface, providing anoverall scale factor for the ventricle.

Previously defined points are used to determine the initial position ofthe model with respect to the images. These are:

-   a) the location of the central axis at the center of the LV in an    apical short axis image-   b) the location of the central axis at the center of the LV in a    basal short axis image-   c) the approximate centroid of the right ventricle-   d) a set of points describing the mitral valve plane.

A model-based coordinate system is then constructed with the originplaced on the central axis of the LV one third of the distance from thebase to the apex. Nodes are placed at equally spaced intervals in thetwo angular coordinates (μ, θ) and at a constant radial coordinate (λ).The centroid of the RV has θ=0 and the extent of the model in the μdirection is governed by the basal margin points. The distance from apexto base is used to determine the focal length of the prolate system andprovides an overall scale factor for the LV.

Each guide point is projected onto the model along lines of constant μand θ and only the λfield is fitted by linear least squares.

As shown at 118 in FIG. 1, the next step is to define one or more guidepoints on one or more slices for which the spatial positions have beendefined. Referring to FIG. 7 the user is presented with the optionindicated at 38 of defining guide points. The user then selects anddisplays in panel 22 any one of the slices stored in the memory. Theimage of the subject organ is displayed in panel 22. A representation ofthe reference model is preferably superimposed on the image of thesubject organ. In the preferred form, a representation of theintersection of the reference model with the image slice superimposed onthe image slice, as indicated by contour lines 39A and 39B.

The user first selects the active surface which is being defined asindicated at 40. In FIG. 8 the user has selected the left ventricularendocardial boundary as the active surface. Using a mouse, the userdefines one or more boundary guide points on the selected slice byclicking in panel 22. These guide points, in combination with thereference model, define the endocardial or epicardial boundaries of theheart. Preferably three to four endocardial boundary guide points andthree to four epicardial boundary guide points are entered for eachslice, although the user is able to enter a large number of boundaryguide points for a particular slice or may instead ignore a slice andenter no boundary guide points for it.

As the user defines the guide points, the system converts these guidepoints to three-dimensional coordinates from the image position in spacefor each boundary guide point, and fits the model by forcing the modelto adhere closely to the guide points, as will be discussed below.

Where a number of image frames are stored in memory, the user mayadvance to the next frame by clicking on the Next Frame option indicatedat 43.

FIGS. 9 and 10 further illustrate the process by which the boundarypoints are defined by the user. The user is defining the endocardialboundary of an image slice different to the one shown in FIG. 7. InFIGS. 9 and 10 the user is defining the epicardial and endocardialboundaries respectively for a further image.

It will be appreciated that where an organ other than the left ventricleis to be modelled, the reference model will be varied. For example,where the invention is used to model the right ventricle, lung and/orkidney, a different reference model will be defined.

The next step indicated at 120 in FIG. 1 is to form an estimate model byfitting the reference model to the guide points. This process could beinitiated automatically whenever the user selects the Next Frame optionindicated at 43 in FIGS. 7 to 10, or may be updated automatically inreal time with the insertion or deletion of a guide point or any changein guide point position.

The preferred method for incorporating the reference model data is tominimise an error function consisting of the sum of a smoothing term anda term penalising the distance between each boundary guide point and thecorresponding reference model position.

The preferred smoothing term penalises changes in slope and curvaturearound the left ventricle, allowing the reference model to realisticallyinterpolate guide point data where the data is sparse.

The preferred penalty is introduced into the least squares method whichpenalises only the sum of the squared deviations from the boundary guidepoints to the reference model surface. In particular, the first andsecond derivatives of the surfaces are constrained to be minimisedwithin the least squares fit to prevent rippling and otherabnormalities. In this sense, the smoothing can be viewed as weightingthe estimate model more toward the reference model than the boundaryguide points so that the reference model is imposed more strongly wherethere are no or insufficient boundary guide points.

One preferred smoothing method is set out more particularly below, inwhich the error function minimised is:

$\begin{matrix}{E = {{S(\lambda)} + {\sum\limits_{g}\left( {{\lambda\left( \xi_{g} \right)} - \lambda_{g}} \right)^{2}}}} & (3)\end{matrix}$where λ_(g) are the positions of the guide point data and λ(ξ_(g)) arethe model positions at element co-ordinates ξ_(g) corresponding toλ_(g). The element co-ordinates are preferably found by projecting theguide data onto the model along lines of constant μ and θ. S(λ) denotesa smoothing term included in the error function to constrain the modelto smoothly interpolate between the sparse guide points.

S(λ) is preferably a weighted Sobolev norm which penalizes thedisplacement of the estimate model from the reference model.

$\begin{matrix}{\;{{S(\lambda)} = {{\int_{\Omega}{\alpha_{1}\left\lbrack \frac{\partial u}{\partial\xi_{1}} \right\rbrack}^{2}} + {\alpha_{2}\left\lbrack \frac{\partial u}{\partial\xi_{2}} \right\rbrack}^{2} + {\beta_{1}\left\lbrack \frac{\partial^{2}u}{\partial\xi_{1}^{2}} \right\rbrack}^{2} + {\beta_{2}\left\lbrack \frac{\partial^{2}u}{\partial\xi_{2}^{2}} \right\rbrack}^{2} + {\gamma_{1}\left\lbrack \frac{\partial^{2}u}{{\partial\xi_{1}}{\partial\xi_{2}}} \right\rbrack}^{2} + {\gamma_{2}\left\lbrack \frac{\partial^{2}u}{{\partial\xi_{1}}{\partial\xi_{3}}} \right\rbrack}^{2} + {{\gamma_{3}\left\lbrack \frac{\partial^{2}u}{{\partial\xi_{2}}{\partial\xi_{3}}} \right\rbrack}^{2}{\mathbb{d}\Omega}}}}} & (4)\end{matrix}$where u=λ−λ* where λ* is the reference model. The weights α₁ and α₂penalize the slope of the displacement field in the circumferential andlongitudinal directions respectively, the weights β₁ and β₂ penalizecurvature and the weights γ₁, γ₂ and γ₃ couple slopes betweendirections. Typical smoothing weights are α₁=α₂=0, β₁=β₂=0.001,γ₁=γ₂=γ₃=0.01.

The resulting estimate model incorporates endocardial and epicardialboundary guide points, the long axis of the left ventricle, referencemodel data and smoothing constraints to produce endocardial andepicardial left ventricular surfaces in three or four dimensions closelyapproximating the true surfaces represented in the images.

Having defined these surfaces, the software may then calculate theintersection of the image slices with the surfaces and display theestimate model as indicated at 122 in FIG. 1. The intersections are eachrepresented by two lines, one line representing the endocardium and theother line representing the epicardium which are close to the edges onthe images.

Having calculated endocardial and epicardial surface boundaries on eachimage, image(s) are then evaluated, preferably by the user, foracceptable contours as indicated at 124 and acceptable image quality asindicated at 126. The software may then perform local image processingas indicated at 128 to further improve the quality of the left ventricleboundary edges displayed in the images. The use of local imageprocessing is not essential to the invention. Where provided, it may beinvoked by the user. An edge is characterized by an abrupt change inintensity indicating a boundary, and is called a discontinuity. Ingeneral an edge is often seen as a slow change in grey level valuesbetween connected pixels. While a boundary edge may be readily apparentto the human eye, it can be difficult for software to detect.

Prior art methods of detecting discontinuities or edges include runninga mask or window over the image, or by applying a known edge enhancingfilter such as a Roberts, Sobel or Laplacian operator. Applying such afilter to an entire image in order to find boundary edges iscomputationally expensive. Using the present invention, the endocardialand epicardial boundaries have been estimated with the estimate model.The software therefore can calculate the likely position of a boundaryedge in an image. An a priori technique may then be applied to guide thesearch for the boundary in the image.

In one method, radial lines may be drawn extending through both theendocardial and epicardial boundaries. An edge filter may be applied atthe intersection of these boundaries and the radial line to determinethe points on the radial line most likely to represent the endocardialand epicardial edges.

Further local processing could also include thresholding, for examplegrey-scale thresholding. All pixel values falling between two thresholdvalues T₁ and T₂ retain their grey-scale values but all pixel valuesoutside this interval are set to zero. Such multiple thresholds may beapplied to reduce the number of grey-level values in an image, therebyenhancing the contrast. Thresholding may be applied, for example, tocontrast the area in the image inside the endocardial boundary, from thearea between the endocardial and epicardial boundaries, and/or the areaoutside the epicardial boundary.

Local image processing and thresholding as described above may generateadditional data points which may be added to the existing boundary guidepoints. The estimate model contour may then be redefined based on theadditional data points so that the relationship to the actual images isimproved.

The additional data points obtained from local image processing andthresholding may be assigned less weight than boundary guide pointsselected by the user, as it will be appreciated that these additionaldata points may be less reliable than those selected by the user. Thegenerated data points may be displayed to the user in order to identifywhere additional guide points are needed to improve the estimate model,for example, where a contour has missed the actual edge.

It will be appreciated that where image processing is performed, it isperformed at a local level. It is assumed that the edge is in aparticular region. Image processing is computationally expensive and inthe past has been performed on an entire image or slice. Using theinvention, it is possible to determine the approximate position of theregion so that image processing can be concentrated on that region,significantly reducing the processing time. Where the local imageprocessing is unable to determine the location of an edge, the estimatemodel will be determined primarily from the guide points defined by theuser, the reference model, and smoothing parameters.

The software may then define the final estimate model surfaces from thefinite element modelling process, with the inclusion of data pointsobtained by local image processing. The intersection of this surfacewith the image slices permits the software to display on the originalimages the left ventricular endocardial and epicardial boundary walls.The step of drawing the estimate model is indicated in FIG. 1 at 122.

The number of guide points can be reduced substantially if the imagesare of good quality and the image processing is able to accuratelydefine the edges and guide the estimate model on to them.

As shown in FIG. 2, the main window presents to the user the option ofviewing the images in three dimensions, indicated at 54. On selectingthis option the user is presented with a 3D viewer window as will bemore particularly described with reference to FIGS. 11 and 12.

Referring to FIG. 11, the window includes panel 56 in which images aredisplayed. The user has the option of selecting image planes as thedesired view as indicated at 124. Where the Image planes option isselected, the user may select which image slices to display in panel 56.In FIG. 11 the user has selected the long axis slice and the fifth shortaxis slice to display in panel 56. Also displayed in panel 56 are theintersections of the estimate model with the original image planes.

Referring to FIG. 12, the user has elected not to display image planes.The image displayed in panel 56 is instead the estimate endocardialsurface rather than individual image planes.

Each boundary or contour is then assessed by the user forappropriateness. If the boundary or contour is unacceptable, the usermay define further boundary guide points until such time as the boundaryor contour is acceptable, as indicated in FIG. 1 at 124.

Left ventricular cardiac volume, for example, may then be estimated bycalculating the volume bounded by the estimate model in the stepindicated at 130 in FIG. 1. Left ventricular wall thickness may also becalculated from the estimate model. Left ventricular mass may becalculated from the difference between the volumes enclosed by theendocardial and epicardial contours multiplied by an appropriateconstant, for example 1.05 g/ml.

It will be appreciated that the volume, wall thickness and mass of theright ventricle may also be calculated in the same way. Where more thanone frame is stored in the memory, the method may be used to measureabnormalities in the left or right ventricle identified through changesin wall thickness over time. The system may also be arranged tocalculate area, curvature, angles and other parameters from the estimatemodel.

The method and system of the invention may further provide the resultsof measurement and analysis in an intuitive, useful and interactive waythat the cardiologist can understand and use in patient management.Referring to FIG. 13, the system may display to a user the volumes ofblood or muscle associated with each imaging slice. These volumes arepreferably determined by calculating the area of the endocardium orepicardium from the estimate model for each slice and multiplying eacharea by the slice thickness.

Individual slice identifiers are indicated at 80 in a display panel. Byclicking in the appropriate box adjacent a slice identifier the user mayview a graph indicated at 82 of volume vs time for the mass, endocardiumor epicardium respectively. Individual boxes indicated at 84 may displaynumerical values for end-diastolic and end-systolic volumes, mass,stroke volume and ejection fraction for each slice.

FIG. 14 illustrates display of data similar to the data of FIG. 13. InFIG. 14 regions or sections of the ventricle are displayed according tothe standard definitions of the American Society of Echocardiographers.The data displayed in FIG. 14 is useful to a cardiologist as the data isindependent of the original scan planes. The data has been acquired bymathematically sampling the model in each of the regions.

FIG. 15 illustrates a further display in which the left ventricle hasbeen folded out onto a flat surface and the wall thickness at everypoint mapped in a different colour or shade. This display cines throughthe cardiac cycle as wall thickness changes. Arbitrary user definedregions can be drawn onto the map and the results shown as a graph, forexample regions A, B and C in FIG. 14. The right ventricle insertionwith the left ventricle is shown by the dots in the image of the leftventricle. Further buttons on the display switch different parts of thedisplay on and off.

The method may also be used to measure characteristics of other organsfor example the lung, the kidney or may be used to measure the wall of ablood vessel. In the case of a kidney the method may be used to measurecortical thickness.

In a further form the invention could be implemented on or associatedwith scanning apparatus. As the scanner produces images, an operatorcould insert guide points and update the estimate model. Areas ofinsufficient data could be identified automatically and the scannerdirected to scan these planes automatically.

The method and system of the invention are particularly suited tomeasuring characteristics of human subjects. It will be appreciated thatthe same method and system could be used to measure characteristics oforgans of other mammals for example rodents, canines and primates. Thesame technique could also be applied to the task of measuring the sizeand/or geometry of a cell.

The foregoing describes the invention including preferred forms thereof.Alterations and modifications as will be obvious to those skilled in theart are intended to be incorporated within the scope hereof as definedby the accompanying claims.

1. A method of assessing one or more characteristic(s) of an organ orpart thereof from multiple images acquired of the organ or part thereof,the method including forming a fit between a reference model of thegeometric shape of the organ or part thereof and a series of acquiredimages of the organ or part thereof by a series of user interactivesteps which consist essentially of: defining the spatial position of atleast two of the acquired images; forming an initial fit between thereference model and the acquired images by displaying one or more of theacquired image(s) to a user, manually user defining one or morereference markers on the acquired image(s), and initially fitting themodel to the acquired image(s) by reference to the reference markers onthe image(s); displaying to a user an acquired image of the subjectorgan or part thereof, the image including at least one organ boundaryderived from the intersection of a surface of the organ with the planeof the image; displaying to the user a representation of the initial fitof the reference model by displaying on the acquired image arepresentation of the intersection of the model with the plane of theimage; manually user-defining one or more reference guide points on auser-selected organ boundary on the image displayed to the user, forwhich the spatial positions have been defined; on user definition of theor each reference guide point, converting the guide point(s) tocoordinates which define the three dimensional position of the guidepoint(s); improving the fit of the model to the guide point(s) to forman improved fit of the model for the organ or part; displaying to theuser a representation of the improved fit of the model by displaying onan acquired image a representation of the intersection of the improvedfit of the model with the plane of the user-selected image; manuallyuser-defining one or more further reference guide points on at least onefurther user-selected image displayed to the user, for which the spatialpositions have been defined; and on user definition of the or eachreference guide point, converting the further guide point(s) tocoordinates which define the three dimensional position of the guidepoint(s); and further improving the fit of the model by fitting themodel to said further reference guide point(s), to thereby form afurther improved fit of the estimate model for the organ or part whichenables assessing the one or more characteristic(s) from the estimatemodel.
 2. A method as claimed in claim 1 wherein the step of forming theinitial fit between the reference model and the images, includes thesteps of defining a point associated with the reference marker(s), oneach of two images defining a reference line in 3-dimensional spacebetween the points, calculating the distance as a function of the lengthof the reference line, and at least approximately matching the scale ofthe reference model and the images according to the distance between thepoints.
 3. A method as claimed in claim 2 wherein the reference modelcomprises a mathematically defined reference model.
 4. A method asclaimed in claim 3 wherein the reference model comprises an ellipsoidhaving a reference line as a central axis and one or more surfacepoints, each surface point specified by a radial distance from thecentral axis.
 5. A method as claimed in claim 1 further comprising thestep of performing image processing on one or more of the images.
 6. Amethod as claimed in claim 1 further comprising the step of calculatingthe volume of the subject organ or part from the estimate model.
 7. Amethod as claimed in claim 1 further comprising the step of calculatingthe mass of the subject organ or part from the estimate model.
 8. Amethod as claimed in claim 1 wherein the subject organ comprises aventricle of the heart and the characteristics measured includeventricular mass, endocardial volume and/or wall thickness of all of theventricle or part thereof.
 9. A method as claimed in claim 1 wherein thesubject organ comprises a ventricle of the heart and the characteristicsmeasured include ventricular abnormalities identified through changes inwall thickness over time.
 10. A method as claimed in claim 1 wherein thesubject organ comprises a kidney and the characteristics measuredinclude cortical thickness.
 11. A system for assessing one or morecharacteristic(s) of an organ or part thereof from multiple imagesacquired of the organ or part thereof by forming a fit between areference model of the geometric shape of the organ or part thereof anda series of acquired images of the organ or part thereof by a series ofuser interactive steps, the system comprising: a memory in which isstored the spatial position of at least two of the images; initialfitting means configured to form an initial fit between the referencemodel and the acquired images by displaying one or more referencemarkers to a user, manually user defining one or more reference markerson the acquired image(s), and initially fitting the model to theacquired image(s) by reference to the reference markers on the image(s);a display configured to display to a user an acquired image of thesubject organ or part thereof, the image including at least one organboundary derived from the intersection of a surface of the organ withthe plane of the user-selected image, the display further configured todisplay to the user a representation of the initial fit of the referencemodel by displaying on the image a representation of the intersection ofthe model with the plane of the image; reference guide point definitionmeans enabling a user to manually define one or more reference guidepoints on a user-selected organ boundary on the image displayed to theuser, for which the spatial positions have been defined; conversionmeans configured, on user definition of the or each reference guidepoint, to convert the guide point(s) to coordinates which define thethree dimensional position of the guide point(s); fit improving meansconfigured, on user definition of the or each reference guide point, toimprove the fit of the model to the guide point(s) to form an improvedfit of the model for the organ or part thereof; the display furtherconfigured, on user definition of the or each reference guide point, todisplay to the user a representation of the improved fit of the model bydisplaying on an acquired image a representation of the intersection ofthe improved fit of the model with the plane of the image; the referenceguide point definition means further configured to enable a user tomanually define one or more further reference guide points on at leastone further image displayed to the user, for which the spatial positionshave been defined; the conversion means further configured, on userdefinition of the or each reference guide point, to convert theadditional guide point(s) to coordinates which define the threedimensional position of the guide points; and the fit improving meansfurther configured, on user definition of the or each reference guidepoint, to further improve the fit of the model by fitting the model tosaid further reference guide point(s), to thereby form a furtherimproved fit of the estimate model for the organ or part which enablesassessing the one or more characteristic(s) from the estimate model. 12.A system as claimed in claim 11 wherein the initial fitting means isconfigured to form the initial fit between the reference model and theimages by defining a reference line in three-dimensional space betweenthe points, calculating the distance as a function of the length of thereference line, and at least approximately matching the scale of thereference model and the images according to the distance between thepoints.
 13. A system as claimed in claim 12 wherein the reference modelcomprises a finite element model.
 14. A system as claimed in claim 13wherein the reference model comprises an ellipsoid having the referenceline as a central axis and one or more surface points, each surfacepoint specified by a radial distance from the central axis.
 15. A systemas claimed in claim 11 further comprising image processing meansconfigured to perform image processing on one or more of the images. 16.A system as claimed in claim 11 further comprising volume calculationmeans configured to calculate the volume of the subject organ or partfrom the estimate model.
 17. A system as claimed in claim 11 furthercomprising mass calculation means configured to calculate the mass ofthe subject organ or part from the estimate model.
 18. A system asclaimed in claim 11 wherein the subject organ comprises a ventricle ofthe heart and the characteristics measured include ventricular mass,endocardial volume and/or wall thickness of all of the ventricle or partthereof.
 19. A system as claimed in claim 11 wherein the subject organcomprises a ventricle of the heart and the characteristics measuredinclude ventricular abnormalities identified through changes in wallthickness over time.
 20. A system as claimed in claim 11 wherein thesubject organ comprises a kidney and the characteristics measuredinclude cortical thickness.
 21. A computer readable medium having storedthereon a computer program for assessing one or more characteristic(s)of an organ or part thereof of a subject from multiple images acquiredof the organ or part thereof by forming a fit between a reference modelof the geometric shape of the organ or part thereof and a series ofacquired images of the organ or part thereof by a series of userinteractive steps, the program comprising: storage means configured tostore the spatial position of at least two of the images; initialfitting means configured to form an initial fit between the referencemodel and the acquired images by displaying at least two of the acquiredimages to a user, manually user defining one or more reference markerson the acquired image(s), and initially fitting the model to theacquired image(s) by reference to the reference markers on the image(s);a display configured to display to a user an acquired image of thesubject organ or part thereof, the image including at least one organboundary derived from the intersection of a surface of the organ withthe plane of the user-selected image, the display further configured todisplay to the user a representation of the initial fit of the referencemodel by displaying on the acquired image a representation of theintersection of the model with the plane of the image; reference guidepoint definition means enabling a user to manually define one or morereference guide points on a user-selected organ boundary on the imagedisplayed to the user, for which the spatial positions have beendefined; conversion means configured, on user definition of the or eachreference guide point, to convert the guide point(s) to coordinateswhich define the three-dimensional position of the guide point(s); fitimproving means configured, on user definition of the or each referenceguide point, to improve the fit of the model to the guide point(s) toform an improved fit of the model for the organ or part thereof; thedisplay further configured, on user definition of the or each referenceguide point, to display to the user a representation of the improved fitof the model by displaying on an acquired image a representation of theintersection of the improved fit of the model with the plane of theuser-selected image; the reference guide point definition means furtherconfigured to enable a user to manually define one or more furtherreference guide points on at least one further acquired image displayedto the user, for which the spatial positions have been defined; theconversion means further configured, on user definition of the or eachreference guide point, to convert the additional guide point(s) tocoordinates which define three-dimensional position of the guide points;and the fit improving means further configured, on user definition ofthe or each reference guide point, to further improve the fit of themodel by fitting the model to said further reference guide point(s), tothereby form a further improved fit of the estimate model for the organor part which enables assessing the one or more characteristic(s) fromthe estimate model.
 22. A computer readable medium as claimed in claim21 wherein the initial-fitting means is configured to form the initialfit between the reference model and the images by defining a pointassociated with the reference marker(s) on each of two images, defininga reference line in three-dimensional space between the points,calculating the distance as a function of the length of the referenceline, and at least approximately matching the scale of the referencemodel and the images according to the distance between the points.
 23. Acomputer readable medium as claimed in claim 22 wherein the referencemodel comprises a finite element model.
 24. A computer readable mediumas claimed in claim 23 wherein the reference model comprises anellipsoid having the reference line as a central axis and one or moresurface points, each surface point specified by a radial distance fromthe central axis.
 25. A computer readable medium as claimed in claim 21further comprising image processing means configured to perform imageprocessing on one or more of the images.
 26. A computer readable mediumas claimed in claim 21 further comprising volume calculation meansconfigured to calculate the volume of the subject organ or part from theestimate model.
 27. A computer readable medium as claimed in claim 21further comprising mass calculation means configured to calculate themass of the subject organ or part from the estimate model.
 28. Acomputer readable medium as claimed in claim 21 wherein the subjectorgan comprises a ventricle of the heart and the characteristicsmeasured include ventricular mass, endocardial volume and/or wallthickness of all of the ventricle or part thereof.
 29. A computerreadable medium as claimed in claim 21 wherein the subject organcomprises a ventricle of the heart and the characteristics measuredinclude ventricular abnormalities identified through changes in wallthickness over time.
 30. A computer readable medium as claimed in claim21 wherein the subject organ comprises a kidney and the characteristicsmeasured include cortical thickness.